Mmm.txt Now

: Transforms complex 3D human motion into a sequence of discrete tokens in a latent space.

This paper introduces a novel paradigm for , focusing on real-time performance and high-fidelity movement. 🧩 Key Components

: A long-standing academic conference; the "long paper" could refer to a specific accepted submission for the upcoming MMM 2026 or previous years. mmm.txt

: The implementation and pre-trained models are available on the Official MMM GitHub .

: Learns to predict randomly masked motion tokens based on pre-computed text tokens. : Transforms complex 3D human motion into a

: Achieves faster generation compared to traditional diffusion-based models.

: Produces highly realistic 3D animations that closely follow text descriptions. mmm.txt

: The masked modeling approach naturally allows for motion in-filling and editing. 🛠️ Resources & Implementation